NeuronDiscover: Agent-in-Twin for Mechanistic Discovery in Neuronal Microenvironments with World Action Models

Mechanistic discovery in neuronal microenvironments requires identifying interventions and observations that discriminate between competing biological explanations. We formulate this problem as joint inference over mechanistic hypotheses and digital twin states, and propose **NeuronDiscover**, an *Agent-in-Twin* framework that integrates scientific reasoning, intervention planning, and evidence acquisition within a mechanism-grounded *World Action Model (WAM)*. NeuronDiscover represents biological knowledge through a structured *Mechanism–Intervention–Observation–Outcome (MIOY)* graph, where experimental evidence updates mechanistic beliefs and guides targeted twin refinement. The agent generates executable intervention programs with explicit verification criteria and adapts its discovery strategy according to uncertainty and observation constraints. We evaluate NeuronDiscover on controlled brain-fluid transport environments with independently verified reference simulations and donor-disjoint cortical neuron electrophysiology recordings. Experiments demonstrate that Agent-in-Twin enables more accurate mechanism identification, selective twin refinement, and efficient intervention prioritization under limited observation budgets. This work introduces an adaptive scientific agent paradigm in which digital twins evolve as mechanistic models through evidence-driven interaction, enabling autonomous discovery in complex neuronal systems. The code is available at https://anonymous.4open.science/r/NeuronDiscover.